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Minimizing Side-Effects in Virtual Knowledge Graph Updates
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AI
DOI：10.1007/978-3-032-08887-1_8.png)
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Abstract
En 中文
In Virtual Knowledge Graphs (VKGs), access to a relational data source is provided through an ontology that is linked to the data source via declarative mappings. While the problem of query answering in VKGs has been studied extensively over the past years, much less attention has been devoted to the problem of instance-level updates over the VKG, realized by updating the underlying data source. Due to the form of VKG mappings, translating VKG updates into a source updates might lead to side-effects in the VKG, i.e., unwanted insertions or deletions. In this paper, we build on a recent proposal for translating VKG updates into source updates, and extend it by introducing the notion of compensation, which are additional updates that aim at reverting side-effects. We provide a novel algorithm relying on multiple levels of compensation and show that it computes source updates with minimal side-effects in the VKG.
Keywords:
Virtual Knowledge Graphs
Instance-level updates
Source updates
Compensation
Side-effects
Journal
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Papers:
15
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